Estimation and detection techniques for doubly-selective channels in wireless communications

A fundamental problem in communications is the estimation of the channel. The signal transmitted through a communications channel undergoes distortions so that it is often received in an unrecognizable form at the receiver. The receiver must expend significant signal processing effort in order to be able to decode the transmit signal from this received signal. This signal processing requires knowledge of how the channel distorts the transmit signal, i.e. channel knowledge. To maintain a reliable link, the channel must be estimated and tracked by the receiver. The estimation of the channel at the receiver often proceeds by transmission of a signal called the 'pilot' which is known a priori to the receiver. The receiver forms its estimate of the transmitted signal based on how this known signal is distorted by the channel, i.e. it estimates the channel from the received signal and the pilot. This design of the pilot is a function of the modulation, the type of training and the channel. [Continues.

Advanced Channel Estimation Techniques for Multiple-Input Multiple-Output Multi-Carrier Systems in Doubly-Dispersive Channels

Flexible numerology of the physical layer has been introduced in the latest release of 5G new radio (NR) and the baseline waveform generation is chosen to be cyclic-prefix based orthogonal frequency division multiplexing (CP-OFDM). Thanks to the narrow subcarrier spacing and low complexity one tap equalization (EQ) of OFDM, it suits well to time-dispersive channels. For the upcoming 5G and beyond use-case scenarios, it is foreseen that the users might experience high mobility conditions. While the frame structure of the 5G NR is designed for long coherence times, the synchronization and channel estimation (CE) procedures are not fully and reliably covered for diverse applications. The research on alternative multi-carrier waveforms has brought up valuable results in terms of spectral efficiency, applications coexistence and flexibility. Nevertheless, the receiver design becomes more challenging for multiple-input multiple-output (MIMO) non-orthogonal multi-carriers because the receiver must deal with multiple dimensions of interference. This thesis aims to deliver accurate pilot-aided estimations of the wireless channel for coherent detection. Considering a MIMO non-orthogonal multi-carrier, e.g. generalized frequency division multiplexing (GFDM), we initially derive the classical and Bayesian estimators for rich multi-path fading channels, where we theoretically assess the choice of pilot design. Moreover, the well time- and frequency-localization of the pilots in non-orthogonal multi-carriers allows to reuse their energy from cyclic-prefix (CP). Taking advantage of this feature, we derive an iterative approach for joint CE and EQ of MIMO systems. Furthermore, exploiting the block-circularity of GFDM, we comprehensively analyze the complexity aspects, and propose a solution for low complexity implementation. Assuming very high mobility use-cases where the channel varies within the symbol duration, further considerations, particularly the channel coherence time must be taken into account. A promising candidate that is fully independent of the multi-carrier choice is unique word (UW) transmission, where the CP of random nature is replaced by a deterministic sequence. This feature, allows per-block synchronization and channel estimation for robust transmission over extremely doubly-dispersive channels. In this thesis, we propose a novel approach to extend the UW-based physical layer design to MIMO systems and we provide an in-depth study of their out-of-band emission, synchronization, CE and EQ procedures. Via theoretical derivations and simulation results, and comparisons with respect to the state-of-the-art CP-OFDM systems, we show that the proposed UW-based frame design facilitates robust transmission over extremely doubly-dispersive channels.:1 Introduction 1 1.1 Multi-Carrier Waveforms 1 1.2 MIMO Systems 3 1.3 Contributions and Thesis Structure 4 1.4 Notations 6 2 State-of-the-art and Fundamentals 9 2.1 Linear Systems and Problem Statement 9 2.2 GFDM Modulation 11 2.3 MIMO Wireless Channel 12 2.4 Classical and Bayesian Channel Estimation in MIMO OFDM Systems 15 2.5 UW-Based Transmission in SISO Systems 17 2.6 Summary 19 3 Channel Estimation for MIMO Non-Orthogonal Waveforms 21 3.1 Classical and Bayesian Channel Estimation in MIMO GFDM Systems 22 3.1.1 MIMO LS Channel Estimation 23 3.1.2 MIMO LMMSE Channel Estimation 24 3.1.3 Simulation Results 25 3.2 Basic Pilot Designs for GFDM Channel Estimation 29 3.2.1 LS/HM Channel Estimation 31 3.2.2 LMMSE Channel Estimation for GFDM 32 3.2.3 Error Characterization 33 3.2.4 Simulation Results 36 3.3 Interference-Free Pilot Insertion for MIMO GFDM Channel Estimation 39 3.3.1 Interference-Free Pilot Insertion 39 3.3.2 Pilot Observation 40 3.3.3 Complexity 41 3.3.4 Simulation Results 41 3.4 Bayesian Pilot- and CP-aided Channel Estimation in MIMO NonOrthogonal Multi-Carriers 45 3.4.1 Review on System Model 46 3.4.2 Single-Input-Single-Output Systems 47 3.4.3 Extension to MIMO 50 3.4.4 Application to GFDM 51 3.4.5 Joint Channel Estimation and Equalization via LMMSE Parallel Interference Cancellation 57 3.4.6 Complexity Analysis 61 3.4.7 Simulation Results 61 3.5 Pilot- and CP-aided Channel Estimation in Time-Varying Scenarios 67 3.5.1 Adaptive Filtering based on Wiener-Hopf Approac 68 3.5.2 Simulation Results 69 3.6 Summary 72 4 Design of UW-Based Transmission for MIMO Multi-Carriers 73 4.1 Frame Design, Efficiency and Overhead Analysis 74 4.1.1 Illustrative Scenario 74 4.1.2 CP vs. UW Efficiency Analysis 76 4.1.3 Numerical Results 77 4.2 Sequences for UW and OOB Radiation 78 4.2.1 Orthogonal Polyphase Sequences 79 4.2.2 Waveform Engineering for UW Sequences combined with GFDM 79 4.2.3 Simulation Results for OOB Emission of UW-GFDM 81 4.3 Synchronization 82 4.3.1 Transmission over a Centralized MIMO Wireless Channel 82 4.3.2 Coarse Time Acquisition 83 4.3.3 CFO Estimation and Removal 85 4.3.4 Fine Time Acquisition 86 4.3.5 Simulation Results 88 4.4 Channel Estimation 92 4.4.1 MIMO UW-based LMMSE CE 92 4.4.2 Adaptive Filtering 93 4.4.3 Circular UW Transmission 94 4.4.4 Simulation Results 95 4.5 Equalization with Imperfect Channel Knowledge 96 4.5.1 UW-Free Equalization 97 4.5.2 Simulation Results 99 4.6 Summary 102 5 Conclusions and Perspectives 103 5.1 Main Outcomes in Short 103 5.2 Open Challenges 105 A Complementary Materials 107 A.1 Linear Algebra Identities 107 A.2 Proof of lower triangular Toeplitz channel matrix being defective 108 A.3 Calculation of noise-plus-interference covariance matrix for Pilot- and CPaided CE 108 A.4 Bock diagonalization of the effective channel for GFDM 109 A.5 Detailed complexity analysis of Sec. 3.4 109 A.6 CRLB derivations for the pdf (4.24) 113 A.7 Proof that (4.45) emulates a circular CIR at the receiver 11

Equalization of doubly selective channels using iterative and recursive methods

Novel iterative and recursive schemes for the equalization of time-varying frequency selective channels are proposed. Such doubly selective channels are shown to be common place in mobile communication systems, for example in second generation systems based on time division multiple access (TDMA) and so-called beyond third generation systems most probably utilizing orthogonal frequency division multiplexing (OFDM). A new maximum likelihood approach for the estimation of the complex multipath gains (MGs) and the real Doppler spreads (DSs) of a parametrically modelled doubly selective single input single output (SISO) channel is derived. Considerable complexity reduction is achieved by exploiting the statistical properties of the training sequence in a TDMA system. The Cramer-Rao lower bound for the resulting estimator is derived and simulation studies are employed to confirm the statistical efficiency of the scheme. A similar estimation scheme is derived for the MGs and DSs in the context of a multiple input multiple output (MIMO) TDMA system. A computationally efficient recursive equalization scheme for both a SISO and MIMO TDMA system which exploits the estimated MGs and DSs is derived on the basis of repeated application of the matrix inversion lemma. Bit error rate (BER) simulations confirm the advantage of this scheme over equalizers which have limited knowledge of such parameters. For OFDM transmission over a general random doubly selective SISO channel, the time selectivity is mitigated with an innovative relatively low complexity iterative method. Equalization is in effect split into two stages: one which exploits the sparsity in the associated channel convolution matrix and a second which performs a posteriori detection of the frequency domain symbols. These two procedures interact in an iterative manner, exchanging information between the time and frequency domains. Simulation studies show that the performance of the scheme approaches the matched filter bound when interleaving is also introduced to aid in decorrelation. Finally, to overcome the peak to average power problem in conventional OFDM transmission, the iterative approach is extended for single carrier with cyclic prefix (SCCP) systems. The resulting scheme has particularly low complexity and is shown by simulation to have robust performance.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Equalization of doubly selective channels using iterative and recursive methods

Novel iterative and recursive schemes for the equalization of time-varying frequency selective channels are proposed. Such doubly selective channels are shown to be common place in mobile communication systems, for example in second generation systems based on time division multiple access (TDMA) and so-called beyond third generation systems most probably utilizing orthogonal frequency division multiplexing (OFDM). A new maximum likelihood approach for the estimation of the complex multipath gains (MGs) and the real Doppler spreads (DSs) of a parametrically modelled doubly selective single input single output (SISO) channel is derived. Considerable complexity reduction is achieved by exploiting the statistical properties of the training sequence in a TDMA system. The Cramer-Rao lower bound for the resulting estimator is derived and simulation studies are employed to confirm the statistical efficiency of the scheme. A similar estimation scheme is derived for the MGs and DSs in the context of a multiple input multiple output (MIMO) TDMA system. A computationally efficient recursive equalization scheme for both a SISO and MIMO TDMA system which exploits the estimated MGs and DSs is derived on the basis of repeated application of the matrix inversion lemma. Bit error rate (BER) simulations confirm the advantage of this scheme over equalizers which have limited knowledge of such parameters. For OFDM transmission over a general random doubly selective SISO channel, the time selectivity is mitigated with an innovative relatively low complexity iterative method. Equalization is in effect split into two stages: one which exploits the sparsity in the associated channel convolution matrix and a second which performs a posteriori detection of the frequency domain symbols. These two procedures interact in an iterative manner, exchanging information between the time and frequency domains. Simulation studies show that the performance of the scheme approaches the matched filter bound when interleaving is also introduced to aid in decorrelation. Finally, to overcome the peak to average power problem in conventional OFDM transmission, the iterative approach is extended for single carrier with cyclic prefix (SCCP) systems. The resulting scheme has particularly low complexity and is shown by simulation to have robust performance

Single-Frequency Network Terrestrial Broadcasting with 5GNR Numerology

L'abstract è presente nell'allegato / the abstract is in the attachmen

Efficient channel equalization algorithms for multicarrier communication systems

Blind adaptive algorithm that updates time-domain equalizer (TEQ) coefficients by Adjacent Lag Auto-correlation Minimization (ALAM) is proposed to shorten the channel for multicarrier modulation (MCM) systems. ALAM is an addition to the family of several existing correlation based algorithms that can achieve similar or better performance to existing algorithms with lower complexity. This is achieved by designing a cost function without the sum-square and utilizing symmetrical-TEQ property to reduce the complexity of adaptation of TEQ to half of the existing one. Furthermore, to avoid the limitations of lower unstable bit rate and high complexity, an adaptive TEQ using equal-taps constraints (ETC) is introduced to maximize the bit rate with the lowest complexity. An IP core is developed for the low-complexity ALAM (LALAM) algorithm to be implemented on an FPGA. This implementation is extended to include the implementation of the moving average (MA) estimate for the ALAM algorithm referred as ALAM-MA. Unit-tap constraint (UTC) is used instead of unit-norm constraint (UNC) while updating the adaptive algorithm to avoid all zero solution for the TEQ taps. The IP core is implemented on Xilinx Vertix II Pro XC2VP7-FF672-5 for ADSL receivers and the gate level simulation guaranteed successful operation at a maximum frequency of 27 MHz and 38 MHz for ALAM-MA and LALAM algorithm, respectively. FEQ equalizer is used, after channel shortening using TEQ, to recover distorted QAM signals due to channel effects. A new analytical learning based framework is proposed to jointly solve equalization and symbol detection problems in orthogonal frequency division multiplexing (OFDM) systems with QAM signals. The framework utilizes extreme learning machine (ELM) to achieve fast training, high performance, and low error rates. The proposed framework performs in real-domain by transforming a complex signal into a single 2–tuple real-valued vector. Such transformation offers equalization in real domain with minimum computational load and high accuracy. Simulation results show that the proposed framework outperforms other learning based equalizers in terms of symbol error rates and training speeds

Enabling Technology and Algorithm Design for Location-Aware Communications

Location-awareness is emerging as a promising technique for future-generation wire­ less network to adaptively enhance and optimize its overall performance through location-enabled technologies such as location-assisted transceiver reconfiguration and routing. The availability of accurate location information of mobile users becomes the essential prerequisite for the design of such location-aware networks. Motivated by the low locationing accuracy of the Global Positioning System (GPS) in dense multipath environments, which is commonly used for acquiring location information in most of the existing wireless networks, wireless communication system-based po­sitioning systems have been investigated as alternatives to fill the gap of the GPS in coverage. Distance-based location techniques using time-of-arrival (TOA) mea­surements are commonly preferred by broadband wireless communications where the arrival time of the signal component of the First Arriving Path (FAP) can be con­verted to the distance between the receiver and the transmitter with known location. With at least three transmitters, the location of the receiver can be determined via trilatération method. However, identification of the FAP’s signal component in dense multipath scenarios is quite challenging due to the significantly weaker power of the FAP as compared with the Later Arriving Paths (LAPs) from scattering, reflection and refraction, and the superposition of these random arrival LAPs’ signal compo­ nents will become large interference to detect the FAP. In this thesis, a robust FAP detection scheme based on multipath interference cancellation is proposed to im­ prove the accuracy of location estimation in dense multipath environments. In the proposed algorithm, the signal components of LAPs is reconstructed based on the estimated channel and data with the assist of the communication receiver, and sub­ sequently removed from the received signal. Accurate FAP detection results are then achieved with the cross-correlation between the interference-suppressed signal and an augmented preamble which is the combination of the original preamble for com­ munications and the demodulated data sequences. Therefore, more precise distance estimation (hence location estimation) can be obtained with the proposed algorithm for further reliable network optimization strategy design. On the other hand, multiceli cooperative communication is another emerging technique to substantially improve the coverage and throughput of traditional cellular networks. Location-awareness also plays an important role in the design and imple­mentation of multiceli cooperation technique. With accurate location information of mobile users, the complexity of multiceli cooperation algorithm design can be dra­matically reduced by location-assisted applications, e.g., automatic cooperative base station (BS) determination and signal synchronization. Therefore, potential latency aroused by cooperative processing will be minimized. Furthermore, the cooperative BSs require the sharing of certain information, e.g., channel state information (CSI), user data and transmission parameters to perform coordination in their signaling strategies. The BSs need to have the capabilities to exchange available information with each other to follow up with the time-varying communication environment. As most of broadband wireless communication systems are already orthogonal frequency division multiplexing (OFDM)-based, a Multi-Layered OFDM System, which is spe­cially tailored for multiceli cooperation is investigated to provide parallel robust, efficient and flexible signaling links for BS coordination purposes. These layers are overlaid with data-carrying OFDM signals in both time and frequency domains and therefore, no dedicated radio resources are required for multiceli cooperative networks. In the final aspect of this thesis, an enhanced channel estimation through itera­ tive decision-directed method is investigated for OFDM system, which aims to provide more accurate estimation results with the aid of the demodulated OFDM data. The performance of traditional training sequence-based channel estimation is often lim­ ited by the length of the training. To achieve acceptable estimation performance, a long sequence has to be used which dramatically reduces the transmission efficiency of data communication. In this proposed method, the restriction of the training se­quence length can be removed and high channel estimation accuracy can be achieved with high transmission efficiency, and therefore it particular fits in multiceli coopera­tive networks. On the other hand, as the performance of the proposed FAP detection scheme also relies on the accuracy of channel estimation and data detection results, the proposed method can be combined with the FAP detection scheme to further optimize the accuracy of multipath interference cancellation and FAP detection

Joint Communication and Positioning based on Channel Estimation

Mobile wireless communication systems have rapidly and globally become an integral part of everyday life and have brought forth the internet of things. With the evolution of mobile wireless communication systems, joint communication and positioning becomes increasingly important and enables a growing range of new applications. Humanity has already grown used to having access to multimedia data everywhere at every time and thereby employing all sorts of location-based services. Global navigation satellite systems can provide highly accurate positioning results whenever a line-of-sight path is available. Unfortunately, harsh physical environments are known to degrade the performance of existing systems. Therefore, ground-based systems can assist the existing position estimation gained by satellite systems. Determining positioning-relevant information from a unified signal structure designed for a ground-based joint communication and positioning system can either complement existing systems or substitute them. Such a system framework promises to enhance the existing systems by enabling a highly accurate and reliable positioning performance and increased coverage. Furthermore, the unified signal structure yields synergetic effects. In this thesis, I propose a channel estimation-based joint communication and positioning system that employs a virtual training matrix. This matrix consists of a relatively small training percentage, plus the detected communication data itself. Via a core semi- blind estimation approach, this iteratively includes the already detected data to accurately determine the positioning-relevant parameter, by mutually exchanging information between the communication part and the positioning part of the receiver. Synergy is created. I propose a generalized system framework, suitable to be used in conjunction with various communication system techniques. The most critical positioning-relevant parameter, the time-of-arrival, is part of a physical multipath parameter vector. Estimating the time-of-arrival, therefore, means solving a global, non-linear, multi-dimensional optimization problem. More precisely, it means solving the so-called inverse problem. I thoroughly assess various problem formulations and variations thereof, including several different measurements and estimation algorithms. A significant challenge, when it comes to solving the inverse problem to determine the positioning-relevant path parameters, is imposed by realistic multipath channels. Most parameter estimation algorithms have proven to perform well in moderate multipath environments. It is mathematically straightforward to optimize this performance in the sense that the number of observations has to exceed the number of parameters to be estimated. The typical parameter estimation problem, on the other hand, is based on channel estimates, and it assumes that so-called snapshot measurements are available. In the case of realistic channel models, however, the number of observations does not necessarily exceed the number of unknowns. In this thesis, I overcome this problem, proposing a method to reduce the problem dimensionality via joint model order selection and parameter estimation. Employing the approximated and estimated parameter covariance matrix inherently constrains the estimation problem’s model order selection to result in optimal parameter estimation performance and hence optimal positioning performance. To compare these results with the optimally achievable solution, I introduce a focused order-related lower bound in this thesis. Additionally, I use soft information as a weighting matrix to enhance the positioning algorithm positioning performance. For demonstrating the feasibility and the interplay of the proposed system components, I utilize a prototype system, based on multi-layer interleave division multiple access. This proposed system framework and the investigated techniques can be employed for multiple existing systems or build the basis for future joint communication and positioning systems. The assessed estimation algorithms are transferrable to all kinds of joint communication and positioning system designs. This thesis demonstrates their capability to, in principle, successfully cope with challenging estimation problems stemming from harsh physical environments

Synchronisation, détection et égalisation de modulation à phase continue dans des canaux sélectifs en temps et en fréquence

Si les drones militaires connaissent un développement important depuis une quinzaine d’année, suivi depuis quelques années par les drones civiles dont les usages ne font que se multiplier, en réalité les drones ont un siècle avec le premier vol d’un avion équipé d’un système de pilotage automatique sur une centaine de kilomètre en 1918. La question des règles d’usage des drones civiles sont en cours de développement malgré leur multiplication pour des usages allant de l’agriculture, à l’observation en passant par la livraison de colis. Ainsi, leur intégration dans l’espace aérien reste un enjeu important, ainsi que les standards de communication avec ces drones dans laquelle s’inscrit cette thèse. Cette thèse vise en effet à étudier et proposer des solutions pour les liens de communications des drones par satellite.L’intégration de ce lien de communication permet d’assurer la fiabilité des communications et particulièrement du lien de Commande et Contrôle partout dans le monde, en s’affranchissant des contraintes d’un réseau terrestre (comme les zones blanches). En raison de la rareté des ressources fréquentielles déjà allouées pour les futurs systèmes intégrant des drones, l’efficacité spectrale devient un paramètre important pour leur déploiement à grande échelle et le contexte spatiale demande l’utilisation d’un système de communication robuste aux non-linéarités. Les Modulations à Phase Continue permettent de répondre à ces problématiques. Cependant, ces dernières sont des modulations non-linéaire à mémoire entraînant une augmentation de la complexité des récepteurs. Du fait de la présence d’un canal multi-trajet (canal aéronautique par satellite), le principal objectif de cette thèse est de proposer des algorithmes d’égalisation (dans le domaine fréquentiel pour réduire leur complexité) et de synchronisation pour CPM adaptés à ce concept tout en essayant de proposer une complexité calculatoire raisonnable. Dans un premier temps, nous avons considéré uniquement des canaux sélectifs en fréquence et avons étudier les différents égaliseurs de la littérature. En étudiant leur similitudes et différences, nous avons pu développer un égaliseur dans le domaine fréquentiel qui proposant les mêmes performances a une complexité moindre. Nous proposons également des méthodes d’estimation canal et une méthode d’estimation conjointe du canal et de la fréquence porteuse. Dans un second temps nous avons montré comment étendre ces méthodes à des canaux sélectifs en temps et fréquence permettant ainsi de conserver une complexité calculatoire raisonnable